Ohio – confirmed – FPCA

## 
## Family: quasipoisson 
## Link function: log 
## 
## Formula:
## daily_count ~ cbgam2 + density + ccvi_quintile + s(time, bs = "tp") + 
##     s(time, county, bs = c("fs"), k = 5, m = 2) + offset(log(population))
## 
## Parametric coefficients:
##                       Estimate Std. Error t value Pr(>|t|)    
## (Intercept)         -0.2481056  1.5692590  -0.158  0.87438    
## cbgam2v1.l1         -0.1655405  0.0238200  -6.950 3.80e-12 ***
## cbgam2v1.l2         -1.2390660  0.0998974 -12.403  < 2e-16 ***
## cbgam2v1.l3          1.0228713  0.2730512   3.746  0.00018 ***
## cbgam2v2.l1         -0.2167554  0.0265919  -8.151 3.87e-16 ***
## cbgam2v2.l2         -1.4623201  0.1098598 -13.311  < 2e-16 ***
## cbgam2v2.l3          1.2753285  0.3146705   4.053 5.08e-05 ***
## cbgam2v3.l1         -0.1976064  0.0274890  -7.189 6.83e-13 ***
## cbgam2v3.l2         -1.4713590  0.1100680 -13.368  < 2e-16 ***
## cbgam2v3.l3          1.2589822  0.3141729   4.007 6.17e-05 ***
## cbgam2v4.l1         -0.3007896  0.0281447 -10.687  < 2e-16 ***
## cbgam2v4.l2         -1.4281939  0.1107210 -12.899  < 2e-16 ***
## cbgam2v4.l3          1.4020933  0.3135844   4.471 7.83e-06 ***
## cbgam2v5.l1         -0.3202169  0.0297952 -10.747  < 2e-16 ***
## cbgam2v5.l2         -1.4732199  0.1102811 -13.359  < 2e-16 ***
## cbgam2v5.l3          1.4714920  0.3140096   4.686 2.81e-06 ***
## cbgam2v6.l1         -0.4566587  0.0591895  -7.715 1.28e-14 ***
## cbgam2v6.l2         -1.9927339  0.1295657 -15.380  < 2e-16 ***
## cbgam2v6.l3          0.9684784  0.3218737   3.009  0.00263 ** 
## density             -0.0007044  0.0004548  -1.549  0.12146    
## ccvi_quintile20-40  -0.0025860  0.2708127  -0.010  0.99238    
## ccvi_quintile40-60  -0.4260916  0.2757714  -1.545  0.12234    
## ccvi_quintile60-80  -0.1037217  0.2749235  -0.377  0.70597    
## ccvi_quintile80-100 -0.6191789  0.2901762  -2.134  0.03287 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Approximate significance of smooth terms:
##                    edf  Ref.df      F p-value    
## s(time)          8.845   8.985 174.28  <2e-16 ***
## s(time,county) 317.116 428.000  15.52  <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## R-sq.(adj) =  0.859   Deviance explained = 83.6%
## -REML =  24068  Scale est. = 6.1145    n = 16530

Time effect

This show the effect of calendar time on incidence of cases/deaths, global and interaction with time:

Time by county interaction

PCA by CCVI quintile interaction

3d plot

Contour plot

By Z-score

By Lag

Overall

crossbasis summary

## CROSSBASIS FUNCTIONS
## observations: 18568 
## groups: 88 
## range: -4.949771 to 5.244575 
## lag period: 0 21 
## total df:  18 
## 
## BASIS FOR VAR:
## fun: cr 
## df: 6 
## knots: -3 -2 -1 0 1 2 3 ... 
## intercept: FALSE 
## fx: FALSE 
## S: 9.876923 -9.507692 4.153846 -1.107692 0.2769231 -0.04615385 -9.507692 ... 
## 
## BASIS FOR LAG:
## fun: cr 
## df: 3 
## knots: 0 7 14 21 
## intercept: FALSE 
## fx: FALSE 
## S: 0.02798834 -0.0244898 0.006997085 -0.0244898 0.02798834 -0.01049563 0.006997085 ...

Attributable Fractions (AF)

AF CI
-0.05 [-0.23, 0.10]

Ohio – confirmed – not_at_home_device_count_change

## 
## Family: quasipoisson 
## Link function: log 
## 
## Formula:
## daily_count ~ cbgam2 + density + ccvi_quintile + s(time, bs = "tp") + 
##     s(time, county, bs = c("fs"), k = 5, m = 2) + offset(log(population))
## 
## Parametric coefficients:
##                       Estimate Std. Error t value Pr(>|t|)    
## (Intercept)         -9.4543073  0.2991433 -31.605  < 2e-16 ***
## cbgam2v1.l1          0.0449994  0.0123990   3.629 0.000285 ***
## cbgam2v1.l2         -0.0053036  0.0129298  -0.410 0.681676    
## cbgam2v1.l3         -0.0077687  0.0208452  -0.373 0.709387    
## cbgam2v2.l1          0.0650969  0.0146151   4.454 8.48e-06 ***
## cbgam2v2.l2          0.0198566  0.0150739   1.317 0.187763    
## cbgam2v2.l3         -0.0311140  0.0232453  -1.339 0.180750    
## cbgam2v3.l1          0.0799138  0.0140899   5.672 1.44e-08 ***
## cbgam2v3.l2          0.0167833  0.0150622   1.114 0.265181    
## cbgam2v3.l3         -0.0454964  0.0225752  -2.015 0.043887 *  
## cbgam2v4.l1          0.0572146  0.0149341   3.831 0.000128 ***
## cbgam2v4.l2          0.0258286  0.0159658   1.618 0.105737    
## cbgam2v4.l3         -0.0247039  0.0229523  -1.076 0.281802    
## cbgam2v5.l1          0.1063144  0.0165658   6.418 1.42e-10 ***
## cbgam2v5.l2          0.0525868  0.0175319   2.999 0.002708 ** 
## cbgam2v5.l3         -0.0344500  0.0245444  -1.404 0.160463    
## cbgam2v6.l1          0.0917621  0.0228128   4.022 5.79e-05 ***
## cbgam2v6.l2          0.0328208  0.0235597   1.393 0.163612    
## cbgam2v6.l3         -0.0337893  0.0361665  -0.934 0.350178    
## density             -0.0005019  0.0004403  -1.140 0.254387    
## ccvi_quintile20-40  -0.0091679  0.2629404  -0.035 0.972186    
## ccvi_quintile40-60  -0.3970620  0.2677627  -1.483 0.138124    
## ccvi_quintile60-80  -0.1578364  0.2668291  -0.592 0.554176    
## ccvi_quintile80-100 -0.5722001  0.2810734  -2.036 0.041790 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Approximate significance of smooth terms:
##                    edf  Ref.df      F p-value    
## s(time)          8.859   8.987  204.9  <2e-16 ***
## s(time,county) 304.254 428.000 3377.7  <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## R-sq.(adj) =  0.812   Deviance explained = 81.7%
## -REML =  24925  Scale est. = 7.2321    n = 16530

Time effect

This show the effect of calendar time on incidence of cases/deaths, global and interaction with time:

Time by county interaction

PCA by CCVI quintile interaction

3d plot

Contour plot

By Z-score

By Lag

Overall

crossbasis summary

## CROSSBASIS FUNCTIONS
## observations: 18568 
## groups: 88 
## range: -7.013312 to 8.403648 
## lag period: 0 21 
## total df:  18 
## 
## BASIS FOR VAR:
## fun: cr 
## df: 6 
## knots: -3 -2 -1 0 1 2 3 ... 
## intercept: FALSE 
## fx: FALSE 
## S: 9.876923 -9.507692 4.153846 -1.107692 0.2769231 -0.04615385 -9.507692 ... 
## 
## BASIS FOR LAG:
## fun: cr 
## df: 3 
## knots: 0 7 14 21 
## intercept: FALSE 
## fx: FALSE 
## S: 0.02798834 -0.0244898 0.006997085 -0.0244898 0.02798834 -0.01049563 0.006997085 ...

Attributable Fractions (AF)

AF CI
-0.02 [-0.10, 0.04]

Missouri – confirmed – FPCA

## 
## Family: quasipoisson 
## Link function: log 
## 
## Formula:
## daily_count ~ cbgam2 + density + ccvi_quintile + s(time, bs = "tp") + 
##     s(time, county, bs = c("fs"), k = 5, m = 2) + offset(log(population))
## 
## Parametric coefficients:
##                       Estimate Std. Error t value Pr(>|t|)    
## (Intercept)         -7.9929480  1.0618633  -7.527 5.38e-14 ***
## cbgam2v1.l1          0.0497405  0.0356118   1.397   0.1625    
## cbgam2v1.l2         -0.0286861  0.0598674  -0.479   0.6318    
## cbgam2v1.l3         -0.1182001  0.0913424  -1.294   0.1957    
## cbgam2v2.l1         -0.0247296  0.0366514  -0.675   0.4999    
## cbgam2v2.l2         -0.0633285  0.0650804  -0.973   0.3305    
## cbgam2v2.l3         -0.1250820  0.1007224  -1.242   0.2143    
## cbgam2v3.l1         -0.0347312  0.0370444  -0.938   0.3485    
## cbgam2v3.l2         -0.0697899  0.0654490  -1.066   0.2863    
## cbgam2v3.l3         -0.1624370  0.1004607  -1.617   0.1059    
## cbgam2v4.l1         -0.0036520  0.0374538  -0.098   0.9223    
## cbgam2v4.l2          0.0020219  0.0662365   0.031   0.9756    
## cbgam2v4.l3         -0.0038659  0.1008362  -0.038   0.9694    
## cbgam2v5.l1         -0.0138600  0.0390379  -0.355   0.7226    
## cbgam2v5.l2         -0.0030113  0.0661258  -0.046   0.9637    
## cbgam2v5.l3          0.0565347  0.1007875   0.561   0.5749    
## cbgam2v6.l1          0.0586247  0.0551625   1.063   0.2879    
## cbgam2v6.l2          0.1488674  0.0843854   1.764   0.0777 .  
## cbgam2v6.l3          0.2493528  0.1256891   1.984   0.0473 *  
## density             -0.0002902  0.0002930  -0.990   0.3221    
## ccvi_quintile20-40   0.0335494  0.2073085   0.162   0.8714    
## ccvi_quintile40-60   0.2513591  0.2127677   1.181   0.2375    
## ccvi_quintile60-80   0.2097265  0.2159804   0.971   0.3315    
## ccvi_quintile80-100  0.1089889  0.2184387   0.499   0.6178    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Approximate significance of smooth terms:
##                   edf  Ref.df       F p-value    
## s(time)          8.83   8.985   130.6  <2e-16 ***
## s(time,county) 319.88 563.000 10452.5  <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## R-sq.(adj) =  0.763   Deviance explained = 55.8%
## -REML =  35194  Scale est. = 10.3      n = 22074

Time effect

This show the effect of calendar time on incidence of cases/deaths, global and interaction with time:

Time by county interaction

PCA by CCVI quintile interaction

3d plot

Contour plot

By Z-score

By Lag

Overall

crossbasis summary

## CROSSBASIS FUNCTIONS
## observations: 24687 
## groups: 115 
## range: -3.915483 to 6.277683 
## lag period: 0 21 
## total df:  18 
## 
## BASIS FOR VAR:
## fun: cr 
## df: 6 
## knots: -3 -2 -1 0 1 2 3 ... 
## intercept: FALSE 
## fx: FALSE 
## S: 9.876923 -9.507692 4.153846 -1.107692 0.2769231 -0.04615385 -9.507692 ... 
## 
## BASIS FOR LAG:
## fun: cr 
## df: 3 
## knots: 0 7 14 21 
## intercept: FALSE 
## fx: FALSE 
## S: 0.02798834 -0.0244898 0.006997085 -0.0244898 0.02798834 -0.01049563 0.006997085 ...

Attributable Fractions (AF)

AF CI
0.36 [0.23, 0.47]

Missouri – confirmed – not_at_home_device_count_change

## 
## Family: quasipoisson 
## Link function: log 
## 
## Formula:
## daily_count ~ cbgam2 + density + ccvi_quintile + s(time, bs = "tp") + 
##     s(time, county, bs = c("fs"), k = 5, m = 2) + offset(log(population))
## 
## Parametric coefficients:
##                       Estimate Std. Error t value Pr(>|t|)    
## (Intercept)         -8.6785296  0.2714111 -31.976   <2e-16 ***
## cbgam2v1.l1          0.0118280  0.0097328   1.215    0.224    
## cbgam2v1.l2          0.0001098  0.0114555   0.010    0.992    
## cbgam2v1.l3          0.0002999  0.0170405   0.018    0.986    
## cbgam2v2.l1          0.0056960  0.0111685   0.510    0.610    
## cbgam2v2.l2         -0.0086949  0.0141383  -0.615    0.539    
## cbgam2v2.l3         -0.0111134  0.0226416  -0.491    0.624    
## cbgam2v3.l1         -0.0077217  0.0110971  -0.696    0.487    
## cbgam2v3.l2         -0.0180543  0.0144857  -1.246    0.213    
## cbgam2v3.l3         -0.0295217  0.0232215  -1.271    0.204    
## cbgam2v4.l1         -0.0081136  0.0119748  -0.678    0.498    
## cbgam2v4.l2         -0.0096718  0.0152620  -0.634    0.526    
## cbgam2v4.l3         -0.0382553  0.0232982  -1.642    0.101    
## cbgam2v5.l1         -0.0074997  0.0142062  -0.528    0.598    
## cbgam2v5.l2         -0.0190017  0.0180209  -1.054    0.292    
## cbgam2v5.l3         -0.0392191  0.0255709  -1.534    0.125    
## cbgam2v6.l1          0.0012517  0.0216489   0.058    0.954    
## cbgam2v6.l2         -0.0233125  0.0266764  -0.874    0.382    
## cbgam2v6.l3         -0.0463473  0.0373738  -1.240    0.215    
## density             -0.0002025  0.0002758  -0.734    0.463    
## ccvi_quintile20-40   0.0573403  0.1971000   0.291    0.771    
## ccvi_quintile40-60   0.2568785  0.2023151   1.270    0.204    
## ccvi_quintile60-80   0.2355733  0.2050587   1.149    0.251    
## ccvi_quintile80-100  0.1609694  0.2075387   0.776    0.438    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Approximate significance of smooth terms:
##                    edf  Ref.df       F p-value    
## s(time)          8.827   8.985   147.6  <2e-16 ***
## s(time,county) 313.700 563.000 30500.1  <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## R-sq.(adj) =  0.757   Deviance explained =   55%
## -REML =  35377  Scale est. = 10.457    n = 22074

Time effect

This show the effect of calendar time on incidence of cases/deaths, global and interaction with time:

Time by county interaction

PCA by CCVI quintile interaction

3d plot

Contour plot

By Z-score

By Lag

Overall

crossbasis summary

## CROSSBASIS FUNCTIONS
## observations: 24687 
## groups: 115 
## range: -5.26114 to 4.783962 
## lag period: 0 21 
## total df:  18 
## 
## BASIS FOR VAR:
## fun: cr 
## df: 6 
## knots: -3 -2 -1 0 1 2 3 ... 
## intercept: FALSE 
## fx: FALSE 
## S: 9.876923 -9.507692 4.153846 -1.107692 0.2769231 -0.04615385 -9.507692 ... 
## 
## BASIS FOR LAG:
## fun: cr 
## df: 3 
## knots: 0 7 14 21 
## intercept: FALSE 
## fx: FALSE 
## S: 0.02798834 -0.0244898 0.006997085 -0.0244898 0.02798834 -0.01049563 0.006997085 ...

Attributable Fractions (AF)

AF CI
0.08 [0.02, 0.14]

South Carolina – confirmed – FPCA

## 
## Family: quasipoisson 
## Link function: log 
## 
## Formula:
## daily_count ~ cbgam2 + density + ccvi_quintile + s(time, bs = "tp") + 
##     s(time, county, bs = c("fs"), k = 5, m = 2) + offset(log(population))
## 
## Parametric coefficients:
##                       Estimate Std. Error t value Pr(>|t|)    
## (Intercept)         -4.9710735  3.1244804  -1.591 0.111642    
## cbgam2v1.l1         -0.3651368  0.1943512  -1.879 0.060311 .  
## cbgam2v1.l2         -0.1442688  0.1977649  -0.729 0.465717    
## cbgam2v1.l3          0.3423535  0.2575586   1.329 0.183807    
## cbgam2v2.l1         -0.4369763  0.2047065  -2.135 0.032816 *  
## cbgam2v2.l2         -0.1245427  0.2098121  -0.594 0.552800    
## cbgam2v2.l3          0.1806986  0.2722340   0.664 0.506859    
## cbgam2v3.l1         -0.3563536  0.2090563  -1.705 0.088306 .  
## cbgam2v3.l2         -0.0833101  0.2151986  -0.387 0.698668    
## cbgam2v3.l3          0.0834321  0.2761956   0.302 0.762601    
## cbgam2v4.l1         -0.3699696  0.2080692  -1.778 0.075419 .  
## cbgam2v4.l2         -0.1014235  0.2135369  -0.475 0.634820    
## cbgam2v4.l3          0.0855269  0.2741439   0.312 0.755064    
## cbgam2v5.l1         -0.1885545  0.2101281  -0.897 0.369566    
## cbgam2v5.l2         -0.1744950  0.2233516  -0.781 0.434671    
## cbgam2v5.l3          0.0034195  0.2953735   0.012 0.990763    
## cbgam2v6.l1         -0.3223380  0.2266019  -1.422 0.154919    
## cbgam2v6.l2         -1.1599821  0.3370980  -3.441 0.000582 ***
## cbgam2v6.l3         -0.1002039  0.5366240  -0.187 0.851876    
## density             -0.0007326  0.0039610  -0.185 0.853277    
## ccvi_quintile20-40  -0.1377679  0.5404441  -0.255 0.798794    
## ccvi_quintile40-60   0.0058617  0.5900717   0.010 0.992074    
## ccvi_quintile60-80  -0.4319927  0.6516090  -0.663 0.507371    
## ccvi_quintile80-100 -0.7240565  0.6607208  -1.096 0.273170    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Approximate significance of smooth terms:
##                   edf Ref.df     F p-value    
## s(time)          8.74   8.96 67.00  <2e-16 ***
## s(time,county) 166.76 218.00 57.98  <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## R-sq.(adj) =   0.69   Deviance explained = 51.5%
## -REML =  14248  Scale est. = 11.412    n = 9360

Time effect

This show the effect of calendar time on incidence of cases/deaths, global and interaction with time:

Time by county interaction

PCA by CCVI quintile interaction

3d plot

Contour plot

By Z-score

By Lag

Overall

crossbasis summary

## CROSSBASIS FUNCTIONS
## observations: 10534 
## groups: 46 
## range: -2.666375 to 3.187262 
## lag period: 0 21 
## total df:  18 
## 
## BASIS FOR VAR:
## fun: cr 
## df: 6 
## knots: -3 -2 -1 0 1 2 3 ... 
## intercept: FALSE 
## fx: FALSE 
## S: 9.876923 -9.507692 4.153846 -1.107692 0.2769231 -0.04615385 -9.507692 ... 
## 
## BASIS FOR LAG:
## fun: cr 
## df: 3 
## knots: 0 7 14 21 
## intercept: FALSE 
## fx: FALSE 
## S: 0.02798834 -0.0244898 0.006997085 -0.0244898 0.02798834 -0.01049563 0.006997085 ...

Attributable Fractions (AF)

AF CI
-0.28 [-0.80, 0.09]

South Carolina – confirmed – not_at_home_device_count_change

## 
## Family: quasipoisson 
## Link function: log 
## 
## Formula:
## daily_count ~ cbgam2 + density + ccvi_quintile + s(time, bs = "tp") + 
##     s(time, county, bs = c("fs"), k = 5, m = 2) + offset(log(population))
## 
## Parametric coefficients:
##                       Estimate Std. Error t value Pr(>|t|)    
## (Intercept)         -7.3585636  0.7044556 -10.446  < 2e-16 ***
## cbgam2v1.l1         -0.0007781  0.0156873  -0.050 0.960441    
## cbgam2v1.l2         -0.0221420  0.0199778  -1.108 0.267748    
## cbgam2v1.l3         -0.0769038  0.0297273  -2.587 0.009698 ** 
## cbgam2v2.l1         -0.0170498  0.0146547  -1.163 0.244682    
## cbgam2v2.l2         -0.0358032  0.0203529  -1.759 0.078590 .  
## cbgam2v2.l3         -0.0237120  0.0317925  -0.746 0.455787    
## cbgam2v3.l1         -0.0245017  0.0141348  -1.733 0.083054 .  
## cbgam2v3.l2         -0.0453016  0.0205697  -2.202 0.027666 *  
## cbgam2v3.l3         -0.0875826  0.0320879  -2.729 0.006356 ** 
## cbgam2v4.l1         -0.0470156  0.0161181  -2.917 0.003543 ** 
## cbgam2v4.l2         -0.0759029  0.0225900  -3.360 0.000783 ***
## cbgam2v4.l3         -0.0940610  0.0328642  -2.862 0.004218 ** 
## cbgam2v5.l1         -0.0536121  0.0187174  -2.864 0.004189 ** 
## cbgam2v5.l2         -0.0819148  0.0254076  -3.224 0.001268 ** 
## cbgam2v5.l3         -0.1169673  0.0361538  -3.235 0.001220 ** 
## cbgam2v6.l1         -0.0201357  0.0258199  -0.780 0.435498    
## cbgam2v6.l2         -0.0635720  0.0348997  -1.822 0.068554 .  
## cbgam2v6.l3         -0.1115645  0.0507456  -2.199 0.027938 *  
## density             -0.0020274  0.0038325  -0.529 0.596821    
## ccvi_quintile20-40  -0.1721584  0.5236662  -0.329 0.742348    
## ccvi_quintile40-60  -0.0310760  0.5724979  -0.054 0.956712    
## ccvi_quintile60-80  -0.4288778  0.6317362  -0.679 0.497226    
## ccvi_quintile80-100 -0.7733048  0.6405953  -1.207 0.227399    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Approximate significance of smooth terms:
##                    edf  Ref.df         F p-value    
## s(time)          8.844   8.981     84.68  <2e-16 ***
## s(time,county) 166.336 218.000 107719.99  <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## R-sq.(adj) =  0.673   Deviance explained = 49.2%
## -REML =  14441  Scale est. = 11.263    n = 9360

Time effect

This show the effect of calendar time on incidence of cases/deaths, global and interaction with time:

Time by county interaction

PCA by CCVI quintile interaction

3d plot

Contour plot

By Z-score

By Lag

Overall

crossbasis summary

## CROSSBASIS FUNCTIONS
## observations: 10534 
## groups: 46 
## range: -5.293829 to 4.110733 
## lag period: 0 21 
## total df:  18 
## 
## BASIS FOR VAR:
## fun: cr 
## df: 6 
## knots: -3 -2 -1 0 1 2 3 ... 
## intercept: FALSE 
## fx: FALSE 
## S: 9.876923 -9.507692 4.153846 -1.107692 0.2769231 -0.04615385 -9.507692 ... 
## 
## BASIS FOR LAG:
## fun: cr 
## df: 3 
## knots: 0 7 14 21 
## intercept: FALSE 
## fx: FALSE 
## S: 0.02798834 -0.0244898 0.006997085 -0.0244898 0.02798834 -0.01049563 0.006997085 ...

Attributable Fractions (AF)

AF CI
-0.02 [-0.14, 0.09]

Indiana – confirmed – FPCA

## 
## Family: quasipoisson 
## Link function: log 
## 
## Formula:
## daily_count ~ cbgam2 + density + ccvi_quintile + s(time, bs = "tp") + 
##     s(time, county, bs = c("fs"), k = 5, m = 2) + offset(log(population))
## 
## Parametric coefficients:
##                       Estimate Std. Error t value Pr(>|t|)    
## (Intercept)         -7.3008451  0.7595928  -9.612  < 2e-16 ***
## cbgam2v1.l1         -0.0067489  0.0328058  -0.206 0.837010    
## cbgam2v1.l2         -0.0387647  0.0624688  -0.621 0.534907    
## cbgam2v1.l3         -0.2073509  0.0885050  -2.343 0.019150 *  
## cbgam2v2.l1         -0.1298959  0.0318574  -4.077 4.57e-05 ***
## cbgam2v2.l2          0.0671417  0.0656669   1.022 0.306578    
## cbgam2v2.l3         -0.2694037  0.0952076  -2.830 0.004665 ** 
## cbgam2v3.l1         -0.0975700  0.0322650  -3.024 0.002498 ** 
## cbgam2v3.l2          0.0706172  0.0661173   1.068 0.285508    
## cbgam2v3.l3         -0.2454733  0.0952109  -2.578 0.009940 ** 
## cbgam2v4.l1         -0.1531421  0.0326987  -4.683 2.84e-06 ***
## cbgam2v4.l2          0.0018073  0.0667207   0.027 0.978390    
## cbgam2v4.l3         -0.1665106  0.0947528  -1.757 0.078881 .  
## cbgam2v5.l1         -0.1655653  0.0341055  -4.855 1.22e-06 ***
## cbgam2v5.l2         -0.1174962  0.0660370  -1.779 0.075216 .  
## cbgam2v5.l3         -0.0645839  0.0949800  -0.680 0.496530    
## cbgam2v6.l1         -0.2264953  0.0676473  -3.348 0.000815 ***
## cbgam2v6.l2         -0.0684587  0.0941513  -0.727 0.467166    
## cbgam2v6.l3          0.2005718  0.1212982   1.654 0.098238 .  
## density             -0.0004831  0.0007048  -0.686 0.493029    
## ccvi_quintile20-40  -0.1092601  0.2661421  -0.411 0.681420    
## ccvi_quintile40-60   0.1219913  0.2636666   0.463 0.643605    
## ccvi_quintile60-80  -0.1207748  0.2731773  -0.442 0.658414    
## ccvi_quintile80-100 -0.3734846  0.2804778  -1.332 0.183008    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Approximate significance of smooth terms:
##                    edf  Ref.df     F p-value    
## s(time)          8.911   8.992 194.3  <2e-16 ***
## s(time,county) 339.389 448.000 281.9  <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## R-sq.(adj) =  0.926   Deviance explained = 83.9%
## -REML =  22259  Scale est. = 4.4652    n = 18018

Time effect

This show the effect of calendar time on incidence of cases/deaths, global and interaction with time:

Time by county interaction

PCA by CCVI quintile interaction

3d plot

Contour plot

By Z-score

By Lag

Overall

crossbasis summary

## CROSSBASIS FUNCTIONS
## observations: 20148 
## groups: 92 
## range: -4.077463 to 3.234633 
## lag period: 0 21 
## total df:  18 
## 
## BASIS FOR VAR:
## fun: cr 
## df: 6 
## knots: -3 -2 -1 0 1 2 3 ... 
## intercept: FALSE 
## fx: FALSE 
## S: 9.876923 -9.507692 4.153846 -1.107692 0.2769231 -0.04615385 -9.507692 ... 
## 
## BASIS FOR LAG:
## fun: cr 
## df: 3 
## knots: 0 7 14 21 
## intercept: FALSE 
## fx: FALSE 
## S: 0.02798834 -0.0244898 0.006997085 -0.0244898 0.02798834 -0.01049563 0.006997085 ...

Attributable Fractions (AF)

AF CI
-0.53 [-0.74, -0.34]

Indiana – confirmed – not_at_home_device_count_change

## 
## Family: quasipoisson 
## Link function: log 
## 
## Formula:
## daily_count ~ cbgam2 + density + ccvi_quintile + s(time, bs = "tp") + 
##     s(time, county, bs = c("fs"), k = 5, m = 2) + offset(log(population))
## 
## Parametric coefficients:
##                       Estimate Std. Error t value Pr(>|t|)    
## (Intercept)         -8.8068680  0.3076318 -28.628  < 2e-16 ***
## cbgam2v1.l1          0.0409080  0.0126175   3.242 0.001188 ** 
## cbgam2v1.l2          0.0143243  0.0134283   1.067 0.286112    
## cbgam2v1.l3         -0.0159244  0.0197479  -0.806 0.420033    
## cbgam2v2.l1          0.0436933  0.0129840   3.365 0.000767 ***
## cbgam2v2.l2          0.0154499  0.0141353   1.093 0.274408    
## cbgam2v2.l3         -0.0893255  0.0208115  -4.292 1.78e-05 ***
## cbgam2v3.l1          0.0371242  0.0129420   2.869 0.004129 ** 
## cbgam2v3.l2          0.0045478  0.0142401   0.319 0.749454    
## cbgam2v3.l3         -0.1101936  0.0205670  -5.358 8.53e-08 ***
## cbgam2v4.l1          0.0218108  0.0138549   1.574 0.115453    
## cbgam2v4.l2          0.0022238  0.0150243   0.148 0.882336    
## cbgam2v4.l3         -0.1137870  0.0207644  -5.480 4.31e-08 ***
## cbgam2v5.l1          0.0459453  0.0154664   2.971 0.002976 ** 
## cbgam2v5.l2          0.0228195  0.0168493   1.354 0.175648    
## cbgam2v5.l3         -0.0827534  0.0228805  -3.617 0.000299 ***
## cbgam2v6.l1         -0.0011756  0.0218131  -0.054 0.957021    
## cbgam2v6.l2         -0.0100365  0.0225304  -0.445 0.655990    
## cbgam2v6.l3         -0.1235514  0.0329734  -3.747 0.000180 ***
## density             -0.0002954  0.0007174  -0.412 0.680503    
## ccvi_quintile20-40  -0.0504230  0.2713492  -0.186 0.852586    
## ccvi_quintile40-60   0.1588427  0.2692094   0.590 0.555175    
## ccvi_quintile60-80   0.0688566  0.2779766   0.248 0.804364    
## ccvi_quintile80-100 -0.2695863  0.2857737  -0.943 0.345512    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Approximate significance of smooth terms:
##                    edf  Ref.df      F p-value    
## s(time)          8.903   8.991  332.9  <2e-16 ***
## s(time,county) 338.173 448.000 9820.9  <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## R-sq.(adj) =  0.914   Deviance explained =   83%
## -REML =  22743  Scale est. = 4.708     n = 18018

Time effect

This show the effect of calendar time on incidence of cases/deaths, global and interaction with time:

Time by county interaction

PCA by CCVI quintile interaction

3d plot

Contour plot

By Z-score

By Lag

Overall

crossbasis summary

## CROSSBASIS FUNCTIONS
## observations: 20148 
## groups: 92 
## range: -5.348223 to 4.980483 
## lag period: 0 21 
## total df:  18 
## 
## BASIS FOR VAR:
## fun: cr 
## df: 6 
## knots: -3 -2 -1 0 1 2 3 ... 
## intercept: FALSE 
## fx: FALSE 
## S: 9.876923 -9.507692 4.153846 -1.107692 0.2769231 -0.04615385 -9.507692 ... 
## 
## BASIS FOR LAG:
## fun: cr 
## df: 3 
## knots: 0 7 14 21 
## intercept: FALSE 
## fx: FALSE 
## S: 0.02798834 -0.0244898 0.006997085 -0.0244898 0.02798834 -0.01049563 0.006997085 ...

Attributable Fractions (AF)

AF CI
0.04 [-0.03, 0.10]

Likelihood Ratio Tests

A lower Resid. Dev indicates a better fit. The first row of each table is for single Metric, the second row is for PCA.

[[1]]

Comparison of single Metric vs. PCA for Ohio
Resid. Df Resid. Dev Df Deviance Pr(>Chi)
16150.05 112024.36 NA NA NA
16138.62 99950.19 11.42611 12074.17 0

[[2]]

Comparison of single Metric vs. PCA for Missouri
Resid. Df Resid. Dev Df Deviance Pr(>Chi)
21673.97 187439.6 NA NA NA
21666.96 183785.0 7.014844 3654.553 0

[[3]]

Comparison of single Metric vs. PCA for South Carolina
Resid. Df Resid. Dev Df Deviance Pr(>Chi)
9142.229 67757.48 NA NA NA
9139.455 64724.88 2.773504 3032.599 0

[[4]]

Comparison of single Metric vs. PCA for Indiana
Resid. Df Resid. Dev Df Deviance Pr(>Chi)
17602.82 74436.15 NA NA NA
17602.24 70416.28 0.5821207 4019.869 0